{"policy_class": {":type:": "<class 'abc.ABCMeta'>", ":serialized:": "gAWVOwAAAAAAAACMIXN0YWJsZV9iYXNlbGluZXMzLmNvbW1vbi5wb2xpY2llc5SMEUFjdG9yQ3JpdGljUG9saWN5lJOULg==", "__module__": "stable_baselines3.common.policies", "__doc__": "\n    Policy class for actor-critic algorithms (has both policy and value prediction).\n    Used by A2C, PPO and the likes.\n\n    :param observation_space: Observation space\n    :param action_space: Action space\n    :param lr_schedule: Learning rate schedule (could be constant)\n    :param net_arch: The specification of the policy and value networks.\n    :param activation_fn: Activation function\n    :param ortho_init: Whether to use or not orthogonal initialization\n    :param use_sde: Whether to use State Dependent Exploration or not\n    :param log_std_init: Initial value for the log standard deviation\n    :param full_std: Whether to use (n_features x n_actions) parameters\n        for the std instead of only (n_features,) when using gSDE\n    :param use_expln: Use ``expln()`` function instead of ``exp()`` to ensure\n        a positive standard deviation (cf paper). It allows to keep variance\n        above zero and prevent it from growing too fast. In practice, ``exp()`` is usually enough.\n    :param squash_output: Whether to squash the output using a tanh function,\n        this allows to ensure boundaries when using gSDE.\n    :param features_extractor_class: Features extractor to use.\n    :param features_extractor_kwargs: Keyword arguments\n        to pass to the features extractor.\n    :param share_features_extractor: If True, the features extractor is shared between the policy and value networks.\n    :param normalize_images: Whether to normalize images or not,\n         dividing by 255.0 (True by default)\n    :param optimizer_class: The optimizer to use,\n        ``th.optim.Adam`` by default\n    :param optimizer_kwargs: Additional keyword arguments,\n        excluding the learning rate, to pass to the optimizer\n    ", "__init__": "<function ActorCriticPolicy.__init__ at 0x7bafca38add0>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7bafca38ae60>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7bafca38aef0>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7bafca38af80>", "_build": "<function ActorCriticPolicy._build at 0x7bafca38b010>", "forward": "<function ActorCriticPolicy.forward at 0x7bafca38b0a0>", "extract_features": "<function ActorCriticPolicy.extract_features at 0x7bafca38b130>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7bafca38b1c0>", "_predict": "<function ActorCriticPolicy._predict at 0x7bafca38b250>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7bafca38b2e0>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7bafca38b370>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7bafca38b400>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x7bafca390f80>"}, "verbose": 1, "policy_kwargs": {}, "num_timesteps": 1015808, "_total_timesteps": 1000000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1714498066153674942, "learning_rate": 0.0003, "tensorboard_log": null, "_last_obs": {":type:": "<class 'numpy.ndarray'>", ":serialized:": "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"}, "_last_episode_starts": {":type:": "<class 'numpy.ndarray'>", ":serialized:": "gAWVgwAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYQAAAAAAAAAAAAAAAAAAAAAAEAAAAAAACUjAVudW1weZSMBWR0eXBllJOUjAJiMZSJiIeUUpQoSwOMAXyUTk5OSv////9K/////0sAdJRiSxCFlIwBQ5R0lFKULg=="}, "_last_original_obs": null, "_episode_num": 0, "use_sde": false, "sde_sample_freq": -1, "_current_progress_remaining": -0.015808000000000044, "_stats_window_size": 100, "ep_info_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "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"}, "ep_success_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "gAWVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="}, "_n_updates": 248, "observation_space": {":type:": "<class 'gymnasium.spaces.box.Box'>", ":serialized:": "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", "dtype": "float32", "bounded_below": "[ True  True  True  True  True  True  True  True]", "bounded_above": "[ True  True  True  True  True  True  True  True]", "_shape": [8], "low": "[-90.        -90.         -5.         -5.         -3.1415927  -5.\n  -0.         -0.       ]", "high": "[90.        90.         5.         5.         3.1415927  5.\n  1.         1.       ]", "low_repr": "[-90.        -90.         -5.         -5.         -3.1415927  -5.\n  -0.         -0.       ]", "high_repr": "[90.        90.         5.         5.         3.1415927  5.\n  1.         1.       ]", "_np_random": null}, "action_space": {":type:": "<class 'gymnasium.spaces.discrete.Discrete'>", ":serialized:": "gAWV2wAAAAAAAACMGWd5bW5hc2l1bS5zcGFjZXMuZGlzY3JldGWUjAhEaXNjcmV0ZZSTlCmBlH2UKIwBbpSMFW51bXB5LmNvcmUubXVsdGlhcnJheZSMBnNjYWxhcpSTlIwFbnVtcHmUjAVkdHlwZZSTlIwCaTiUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYkMIBAAAAAAAAACUhpRSlIwFc3RhcnSUaAhoDkMIAAAAAAAAAACUhpRSlIwGX3NoYXBllCmMBWR0eXBllGgOjApfbnBfcmFuZG9tlE51Yi4=", "n": "4", "start": "0", "_shape": [], "dtype": "int64", "_np_random": null}, "n_envs": 16, "n_steps": 1024, "gamma": 0.999, "gae_lambda": 0.98, "ent_coef": 0.01, "vf_coef": 0.5, "max_grad_norm": 0.5, "batch_size": 64, "n_epochs": 4, "clip_range": {":type:": "<class 'function'>", ":serialized:": "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"}, "clip_range_vf": null, "normalize_advantage": true, "target_kl": null, "lr_schedule": {":type:": "<class 'function'>", ":serialized:": "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"}, "system_info": {"OS": "Linux-6.1.58+-x86_64-with-glibc2.35 # 1 SMP PREEMPT_DYNAMIC Sat Nov 18 15:31:17 UTC 2023", "Python": "3.10.12", "Stable-Baselines3": "2.0.0a5", "PyTorch": "2.2.1+cu121", "GPU Enabled": "True", "Numpy": "1.25.2", "Cloudpickle": "2.2.1", "Gymnasium": "0.28.1", "OpenAI Gym": "0.25.2"}}